Compressing data in a wireless network

ABSTRACT

A distinguished node is dynamically selected from a subset of nodes in a wireless network. Data samples from the subset of nodes are received in view of the distinguished node status. At least one estimate is generated from the data samples and the data samples are compressed conditioned on the estimate.

BACKGROUND

Data compression is the process of encoding information using fewer bits(or other information-bearing units) than an unencoded representation ofthe information would use, through the use of specific encoding schemes.Data compression can be burdensome in various power-constrainedapplications.

BRIEF DESCRIPTION OF DRAWINGS

The following description includes discussion of figures havingillustrations given by way of example of implementations of embodimentsof the invention.

FIG. 1 is a block diagram illustrating a system according to variousembodiments.

FIG. 2 is a block diagram illustrating a system according to variousembodiments.

FIG. 3 is a flow diagram of operation in a system according to variousembodiments.

FIG. 4 is a flow diagram of operation in a system according to variousembodiments.

DETAILED DESCRIPTION

As used herein, a sensor network refers to a network of nodes in whichat least some of the nodes collect sensory data. In many situations, aplurality, majority or even all of the nodes in a sensor network collectsensory data. Sensory data may include external sensory data obtained bymeasuring/detecting natural sources such as temperature, sound, wind,seismic activity or the like. Sensory data may also include externalsensory data obtained by measuring/detecting man-made sources such aslight, sound, various frequency spectrum signals, etc. Sensory data mayalternatively include data related to measuring/detecting sourcesinternal to a sensor node (e.g., traffic flow on a network, etc.).

Frequently, in wireless sensor networks, the data collected byneighboring sensor nodes is statistically correlated. This correlationmay be useful for reducing network bandwidth requirements via datacompression. However, taking advantage of this correlation viacompression often leads to complex algorithms that are not compatiblewith usage scenarios in which the nodes are power-constrained.

Wireless sensor networks represent a practical example of the tradeoffbetween data compression and power usage. Data compression reduces theamount of data to be transmitted between wireless nodes. Given thatwireless transmission can be a power intensive operation, any reductionin the amount of data being transmitted can reduce the power needed toperform the transmission. However, in networks that use complexalgorithms to leverage correlated data for better compression, thecomplex encoders that perform the data compression use more power,potentially offsetting the power saved by reducing the amount of databeing transmitted. Accordingly, embodiments described herein exploitcorrelation in sensory data between neighboring nodes while conservingpower in the encoding/compression process. As used herein, the term“correlation” refers to statistical dependencies between various samplesof data.

FIG. 1 is a block diagram illustrating a system according to variousembodiments. As shown, wireless network 100 includes a plurality ofwireless network nodes 110 and 120-126. Network nodes 120-126 may be thesame or similar in form, function, size and features as network node110, with the exception of node 110 representing a distinguished node.In other words, network node 110 is illustrated in more detail in FIG. 1without necessarily having any different components or functionalitythan network nodes 120-126. Distinguished nodes will be discussed inmore detail herein.

In general, network nodes in network 100 collect or otherwise obtainsensory data. Given the potentially limited processing capabilitiesand/or power available at each of the network nodes, embodimentsdescribed herein seek to aggregate sensory data and deliver it to aprocessing station 130 while minimizing power consumption. In variousembodiments, sensory data collected at respective network nodes (e.g.,nodes in a cluster) is sent to a distinguished node. The distinguishednode exploits correlation in the sensory data received from respectivenetwork nodes and compresses the received sensory data. Compressed datais transmitted to processing station 130 where it may be decompressed,further processed, analyzed, further compressed, and/or stored.

Accordingly, negotiation module 112 negotiates (perhaps dynamically,based on changing conditions) a distinguished node status with at leasta subset of other nodes 120-126. For example, the dotted outlineillustrated in FIG. 1 might represent a subset or cluster of nodes innetwork 100 (nodes 110, 120 and 122). In various embodiments, eachcluster or subset of nodes negotiates (via respective negotiationmodules) a distinguished node status for the cluster or subset based onone or more factors. The distinguished node status may be negotiateddynamically, in various embodiments, based on changing externalconditions, changing node conditions, and/or other suitable factors.

In one example, the distinguished node status is negotiated based onphysical location of a node relative to other nodes (e.g., choose thenode at the center of a cluster of nodes as the distinguished node). Inanother example, the distinguished node status is negotiated based onphysical location of a node (e.g., a sensor node) relative to a sourceof sensory data (e.g., choose the node closest to the source as thedistinguished node and have the distinguished node collect data from itsneighbors—data from nodes further away might be discarded due to poorsignal-to-noise ratio, or SNR). In yet another example, thedistinguished node status could be negotiated based on SNR (e.g., choosethe node with the lowest SNR as the distinguished node). As a result ofits distinguished node status, this node could also avoid the collectionof sensory data to save power.

Another factor that could affect negotiation of distinguished nodestatus is the amount of power available at each node participating inthe negotiation. For example, the node with the largest amount of poweravailable could be negotiated as the distinguished node so that it canperform data compression (which can be a power-intensive operation).

Yet another factor that could affect negotiation of distinguished nodestatus is a distance (e.g. Euclidean distance, wireless channelattenuation, number of hops, etc.) of a node to a processing station(e.g. processing station 130). The node with the smallest distance to aprocessing station or other destination could be selected as thedistinguished node to minimize the transmission power used to sendcompressed data to the processing station.

Thus, in view of one or more of factors, negotiation module 112negotiates with other nodes to determine a distinguished status (e.g.,distinguished node or not distinguished node) for node 110. Assumingnode 110 becomes the distinguished node, receiver 114 receives sensorydata from other nodes in the network 100. In certain embodiments,receiver 114 may only receive sensory data from other nodes in the samecluster or from neighboring nodes.

Estimation module 116 generates at least one estimate from the sensorydata. As discussed above, sensory data from neighboring nodes may becorrelated. For example, if nodes 110 and 120-126 are all measuringtemperature, it is foreseeable that temperature data from node 110 willbe similar to temperature data collected at node 120, etc. To takeadvantage of this correlation, estimation module 116 generates at leastone estimate of the data to be compressed, for example, by calculatingthe average of at least some of the received sensory data (e.g., averagetemperature). Conditioned on the estimate, data compressor 118compresses sensory data received from all neighboring nodes (e.g., nodesin the cluster of the distinguished node). For example, the estimatemight be used to differentially compress the sensory data, e.g.,computing D_(i)=X_(i)−Y_(i), where X_(i) is a sample of the sensory data(e.g., at time index i) to compress, Y_(i) is a sample of the estimate,and D_(i) is differential data or prediction error data, which can thenbe compressed using an entropy code tailored to the statisticalproperties of the differential data as specified by a probabilitydistribution P(D_(i)). The code would assign shorter codewords todifferential data values having higher probabilities as specified byP(D_(i)). Typically if the absolute value of differential data valueD_(i)=d_(i) is smaller than the absolute value of differential datavalue D_(i)=d₂ then P(d₁)>P(d₂). The estimate may also be compressedsince it is required to reconstruct the sensory data from thedifferential data. The compressed sensory data is thus comprised of thecompressed estimate and the compressed differential data. Transmitter114 sends compressed sensory data corresponding to all sensory datareceived from neighboring nodes to processing station 130.

FIG. 2 is a block diagram illustrating a system according to variousembodiments. Wireless network 200 includes wireless network nodes 210and 240-246. In general, network nodes in network 200 collect orotherwise obtain data (e.g., sensory data). Given the finite processingcapabilities and/or power available at wireless network nodes, sensorydata is aggregated and delivered to processing station 250 whileminimizing power consumption.

Negotiation module 212 dynamically negotiates a distinguished nodestatus with at least a subset of other nodes 240-246. In variousembodiments, each cluster or subset of nodes negotiates (via respectivenegotiation modules) a distinguished node status for that cluster orsubset based on one or more factors. The distinguished node status maybe negotiated based on physical location of a node relative to othernodes or relative to a signal source. Alternatively, the distinguishednode status could be negotiated as the node (e.g., sensor node) with thelowest SNR (signal-to-noise ratio) which might coincide with this nodeavoiding the collection of sensory data to save power.

Power monitor 220 provides power information for the wireless node tonegotiation module 212. For example, node 210 may be a remote wirelessnode with finite battery power. Power monitor 220 may provideinformation related to the amount of remaining power available in thenode's battery, the rate of use of power by node 210, and/or otherpower-related information. Such information could be used, for example,in an automatic negotiation that determines that the node with thelargest amount of power available should be designated as thedistinguished node. Having more power available may allow the node to bein a better position to perform data compression (which can be apower-intensive operation).

Yet another factor that could affect negotiation of distinguished nodestatus is a distance (e.g. Euclidean distance, wireless channelattenuation, number of hops, etc.) of a node to a processing station(e.g. processing station 250). The node with the smallest distance to aprocessing station or other destination could be selected as thedistinguished node to minimize the transmission power used to sendcompressed data (and/or other data) to the processing station.

Merely by way of example, assume that node 210 is selected as thedistinguished node for wireless network 200 (or a cluster within network200). Accordingly, estimation module 216 uses sensory data, includingdata received from other nodes, to generate an estimate of the sensorydata to be compressed. Sensory data received from other nodes may be atleast partially compressed in some embodiments.

As discussed above, sensory data from neighboring nodes may becorrelated. For example, if nodes in network 200 are measuring seismicactivity, it is foreseeable that seismic data from node 210 will besimilar to seismic data collected at node 246, etc. In addition tospatial correlation of sensory data between nodes (e.g., neighboringnodes), there may also exist temporal correlation among sensorydata—both from a single node and from a collection of nodes.Sample-shifting module 226 dimensionally shifts (e.g., in time, space,etc.) sensory data samples to align them for improved conditionalcompression or differential compression. For example, if sensory datasamples collected at neighboring nodes are essentially time-shiftedversions of each other, then time-shifting the samples into alignmentcan reduce the differences between samples when performing differentialcompression.

Sensor 222 collects sensory data for node 210. Sensor 222 could be anytype of sensor to collect sensory data including, but not limited to, atemperature sensor, a seismic activity sensor, an accelerometer, agyroscope, a chemical sensor, a pressure sensor, a light or infraredsensor, etc. Tagging module 224 tags collected sensory data with one ormore types of metadata (i.e., data about data). For example, networknode 210 may be equipped with a clock that is synchronized on network200 or it may have access to a clock signal. Thus, tagging module maytag sensory data with a timestamp or other suitable timing information.Additionally, node 210 may have modules and/or components to determineor that have access to location information for node 210. For example,node 210 could be equipped with a GPS (Global Positioning System)receiver or it may have processing tools to triangulate or otherwisedetermine its position/location without the direct or even indirect useof GPS.

Estimation module 216 generates an estimate of the sensory data viastatistical modeling. For example, if the sensory data were temperaturedata, then the estimate could be an average temperature value for allsensory data, sensory data over a certain time period, sensory data froma location or group of locations, etc. The estimate may be based onsamples shifted by sample-shifting module 226. Conditioned on theestimate, data compressor 218 compresses sensory data. Where shiftedsamples are used, compressor 218 includes information about the shiftsused to produce the estimate when compressing the sensory data. Thisallows the shifted samples to be reconstructed in their unshifted formduring decoding.

In some embodiments, transmitter 214 explicitly sends the estimate (incompressed or uncompressed form) along with compressed sensory data toprocessing station 250. In such embodiments, processing station 250 usesthe estimate to decompress the compressed sensory data.

In other embodiments, the compressed data includes sufficientinformation for processing station 250 to reconstruct the estimate andthen decompress the sensory data based on the estimate. For example, ifsensor 222 collects temperature data, then at time t=0, the estimate mayrepresent an average temperature, Tavg(0), corresponding to the averageof temperatures T(i,0) at time 0 for other nodes, with i being the nodeindex or number, i=1, 2, . . . , n. Compressor 218 then differentiallycompresses collected temperature data by computingTc(i,0)=T(i,0)−Tavg(0), where again i is the node number. Transmitter214 then sends Tavg(0) and a compressed version of the differential dataTc(i,0) for i=1, 2, . . . , n to processing station 250. At time t=1,compressor 218 computes Tavg(1) (average of node temperatures T(i,1) attime 1) and Tc(i,1)=T(i,1)−Tavg(1). Transmitter 214 then sends acompressed version of the difference (Tavgc(1)=(Tavg(1)−Tavg(0)) and acompressed version of the difference Tc(i,1) for i=1, 2, . . . , n toprocessing station 250. Accordingly, even though Tavg(1) is notexplicitly sent to processing station 250, it can be reconstructed fromthe information already received and decompressed, namely Tavg(0) andTavgc(1), according to Tavg(1)=Tavg(0)+Tavgc(1). Alternatively, Tavg(1)could be differentially compressed by compressor 218 with respect tosome other function of the time 0 node temperatures T(i,0)'s, and notnecessarily the average of these temperatures. Let f denote such afunction. Using this notation, 218 would computeTagvc(1)=Tavg(1)−f(T(1,0),T(2,0), . . . ,T(n,0)) and transmitter 214would send a compressed version of Tavgc(1) determined in this manner toprocessing station 250. Processing station 250 could then reconstructTavg(1) by decompressing Tavgc(1), computing f(T(1,0),T(2,0), . . .,T(n,0)) using the previously decoded and available temperaturesT(1,0),T(2,0), . . . ,T(n,0), and determining Tavg(1) asTavg(1)=Tavgc(1)+f(T(1,0),T(2,0), . . . ,T(n,0)).

The various network node modules and components (negotiation,estimation, power monitor, etc.) of FIG. 1 and FIG. 2 may be implementedas one or more software modules, hardware modules, special-purposehardware (e.g., application specific hardware, application specificintegrated circuits (ASICs), embedded controllers, hardwired circuitry,etc.), or some combination of these. Additionally, various functions andoperations of described embodiments may be implemented as instructionsexecutable by a processor (e.g., processor 228) and stored on acomputer-readable storage medium (e.g., memory 230).

FIG. 3 illustrates a flow diagram of operation in a system according tovarious embodiments. FIG. 3 includes particular operations and executionorder according to certain embodiments. However, in differentembodiments, other operations, omitting one or more of the depictedoperations, and/or proceeding in other orders of execution may also beused according to teachings described herein.

A distinguished node is selected 310 from a plurality of nodes in awireless network. In various embodiments, the nodes in the wirelessnetwork are sensor nodes that collect sensory data. However, in certainembodiments, it is not necessary that all nodes or even some of thenodes be sensor nodes. As discussed above, the distinguished node may beselected based on a variety of factors including, but not limited to,node location (e.g., relative to other nodes, signal sources, etc.),node power (e.g., relative to other nodes) and the like. Once adistinguished node has been established, other nodes in the network,perhaps those belonging to a particular cluster of nodes, send datasamples to the distinguished node. The distinguished node receives 320the data samples and generates 330 an estimate from the data samples.For example, the data samples may be processed for statisticalcorrelation and the estimate is generated there from.

Statistical dependence between data samples may then be leveraged tooptimize data compression. Indeed, data samples received fromneighboring nodes (e.g., all nodes in the cluster of the distinguishednode) are compressed 340 conditioned on the estimate. In variousembodiments, data samples may be differentially compressed. Whilevarious types of compression (e.g., lossy or lossless) could be used inembodiments described herein, lossy compression may provide more powerconservation and better compression ratios than other modes ofcompression.

Not only does compressing the data samples conserve power, but in viewof wireless bandwidth limitations, a greater degree of compressionminimizes the amount of data transmitted on a wireless channel. Thus,compressing data in view of multiple dimensional shifts (e.g., shiftingdata samples in space, time, etc.) may further conserve power andwireless bandwidth. As used herein, dimensional-shifting refers toshifting sensory data samples in view of one or more dimensions. Forexample, data samples from different nodes may be shifted in time (i.e.,a temporal dimension) to leverage temporal dependencies between samplesduring compression (e.g., differential compression). Similarly, datasamples may be shifted in space (i.e., a spatial dimension) to leveragespatial dependencies between samples during compression. One specificexample of compressing data in view of sample-shifting in multipledimensions is the MPEG (Moving Picture Experts Group) video codingstandard.

FIG. 4 illustrates a flow diagram of operation in a system according tovarious embodiments. FIG. 4 includes particular operations and executionorder according to certain embodiments. However, in differentembodiments, other operations, omitting one or more of the depictedoperations, and/or proceeding in other orders of execution may also beused according to teachings described herein.

In a wireless network of nodes, a node is selected 410 as adistinguished node. The distinguished node may represent a cluster ofnodes or the entire network of nodes. In embodiments where adistinguished node represents a cluster of nodes, more than onedistinguished node may be selected within the wireless network. Adistinguished node may be selected in view of a variety of factorsdescribed herein (e.g., node location, node power, etc.). The selectionof a distinguished node for a cluster of nodes may involve nodes in thecluster exchanging messages (e.g., control messages). For example, ifavailable power is the criteria for becoming a distinguished node, eachnode in the cluster may send a message to the other nodes indicating howmuch power the node has available. One node may collect the messages anddetermine which node has the most power and notify the other nodes. Or,each node could collect the messages and compare its own power levelwith power levels from other nodes. In such case, a node could tagitself as the distinguished node if its power level is higher than allother nodes. If a different node has a higher power level, the node cantag itself as a non-distinguished node or do nothing. Other selectionprocesses and/or methods among the nodes could be used todetermine/select the distinguished node in different embodiments. If thedistinguished node status is self-designated, the distinguished node maynotify the other nodes that it has claimed the role of distinguishednode.

The node selected as the distinguished node receives 420 data samplesfrom other nodes (either directly or indirectly). For example, thedistinguished node might receive data samples from its immediateneighbors. In various embodiments, received data samples aredimensionally shifted 430 to take advantage of any correlation in thedata. For example, if the nodes collect seismic wave data over time,seismic waves arrive at different sensors with different delays. Inother words, seismic waves arriving at neighboring sensors are likelytime-shifted versions of one another. Thus, by time-shifting seismicwave samples to bring them into alignment with each other, thecorrelation (i.e., similarities) in the samples can be better leveragedfor compression. For example, time-aligned samples can be used as abasis for computing the estimated data for differential compression ofthe received data samples. Differential compression can be applied usingtime aligned estimated data as well as time-aligned received sensorydata. The compressed data should include a description of the parametersof any time-aligning that may have to be reversed to reconstruct theunshifted sensory data. Other types of dimensional shifting can besimilarly used (e.g., spatial-shifting, etc.) to improve datacompression.

In view of the shifted data samples (although it is not necessary thatdata samples be shifted), an estimate is generated 440. The estimatecould be an average value of the set (or a subset) of shifted datasamples or other suitable representation of the set (or subset) ofshifted data samples. Generating the estimate, in various embodiments,may also involve computing the statistical correlation in the datasamples. Conditioned on the estimate, data samples are compressed 450 bythe distinguished node. The compression can be lossy or lossless;however, lossy compression may conserve power at the distinguished nodeand save network bandwidth while providing sufficiently accurate valuesupon decompression.

In other embodiments, generating the estimate involves generating astatistical model (e.g., conditional probability distributions for datasamples conditioned on the estimate generated and/or previouslyprocessed data samples and/or on further estimates derived thereof).Compression 450 may then be based on the statistical model, for example,by assigning shorter codes to conditionally likelier sample values.

More particularly, statistical dependencies may be modeled betweensensor data samples from one particular node (e.g., at different times)or between sensor data samples and the estimate (e.g., at the sametime). In various embodiments, to compress a sample i collected by afirst sensor node, the statistical model may include dependencies withrespect to the i−1 and i−2 samples of the first node (temporaldependencies). The statistical model might also include (oralternatively include) dependency with respect to sample i of theestimated data (spatial dependencies). The statistical model may consistof a conditional probability distribution P(X_(i)|X_(i-1), X_(i-2),Y_(i)), where X_(j) denotes the sample j collected by the first sensornode, and Y_(i) denotes the sample i of the estimated data. Other modelscould exploit other dependencies, either in the same sensor or acrosssensors or with respect to the estimate.

A data compressor (e.g., data compressor 218 of FIG. 2) encodes the datasequence X_(i) based on the statistical model using an entropy codermatched to the conditional probability distribution P(X_(i)|X_(i-1),X_(i-2), Y_(i)). In various embodiments, the entropy coder mightimplement a Huffman code or an arithmetic code. In another embodiment,an estimate X_(i)′ of X_(i) may be generated as a function of X_(i-1),X_(i-2), and Y_(i) and a probability distribution P′(X_(i)−X_(i)′), andan entropy coder of a compressor (e.g., compressor 218) may be matchedto this probability distribution. In yet another embodiment, a datacompressor may code the difference X_(i)−X_(i)′ with a Golomb code.

Compressed data samples are transmitted 460 to a processing station. Thetransmission of compressed samples may include sufficient information toreproduce the estimate derived by the distinguished node. The processingstation may not have the same or similar power restrictions as thedistinguished node and other nodes in the network. Regardless, theprocessing station decodes the compressed data samples. Thedecoded/decompressed data samples can then be compiled and/or used invarious useful ways. For example, the data samples can be used togenerate a map of a geographic area that illustrates data from thesamples (e.g., a temperature map, a seismic vibrations map, etc.).

The invention claimed is:
 1. A wireless node, comprising: a negotiationmodule to dynamically negotiate a distinguished node status with othernodes in a wireless network to designate a particular distinguished nodeamong the nodes in the network; a receiver to receive sensol3, data fromother nodes in the wireless network when the wireless node is theparticular node in the network having the distinguished node status; anestimation module to generate at least one estimate from the sensorydata received from the other nodes in the network by exploitingcorrelation in the sensory data from different nodes that arises fromstatistical dependency between various samples of data in the sensor,data; a data compressor to compress sensory data using the estimate asan input to a compression process; and a transmitter to send compressedsensory data to a processing station; wherein the dynamic negotiation ofthe distinguished node status is based on power available in at leastsome of the nodes in the wireless network.
 2. The wireless node of claim1, further comprising: a power monitor to provide power information forthe wireless node to the negotiation module.
 3. The wireless node ofclaim 1, further comprising: a sensor to collect sensory data; and atagging module to tag sensory data.
 4. The wireless node of claim 3, thetagging module to tag sensory data with one or more of: a timestamp; anda location tag.
 5. A non-transitory computer-readable storage mediumcontaining instructions that, when executed, cause a computer to:dynamically negotiate a distinguished node status with other nodes in awireless network to designate a particular node among the nodes in thenetwork as having the distinguished node status; collect, in theparticular node having the distinguished node status, sensory data fromthe other nodes in the network; generate at least one estimate based atleast in part on the sensory data received from other nodes in thewireless network; and compress sensory data conditioned on the estimate,such that the estimate is used as an input to a compression processcompressing the sensory data; wherein the dynamic negotiation of thedistinguished node status is based on power available in at least someof the nodes in the wireless network.
 6. The computer-readable storagemedium of claim 5, wherein the instructions that cause the compressionof sensory data comprise further instructions that cause the computerto: compress sensory data according to at least one of spatialcorrelation of sensory data collected at different nodes, and timecorrelation of sensory data.
 7. The computer-readable storage medium ofclaim 5, wherein the dynamic negotiation of the distinguished nodestatus is based on physical location of a node relative to one or moreof: other nodes in the wireless network; a signal source for sensorydata; and a processing station.
 8. The computer-readable storage mediumof claim 5, wherein the instructions that cause the generation of theestimate comprise further instructions that cause the computer to:generate an average of the sensory data.
 9. The computer-readable mediumof claim 5, wherein the instructions that cause the generation of theestimate include further instructions that cause the computer to:determine conditional probability distributions of the sensory datareceived from other nodes in the wireless network conditioned on theestimate.